Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Tulokset 1 - 3 kokonaismäärästä 19
Sivu 56
Thus , we see that the optimum discriminant functions for normal patterns are
quadric functions . 3 . 9 Some special cases involving identical covariance
matrices For the optimum discriminant functions for normal patterns , expansion
of Eq . ( 3 ...
Thus , we see that the optimum discriminant functions for normal patterns are
quadric functions . 3 . 9 Some special cases involving identical covariance
matrices For the optimum discriminant functions for normal patterns , expansion
of Eq . ( 3 ...
Sivu 58
where N . is the number of patterns in the training subset X ; ; ( X ) ; is called the
sample mean ( or center of gravity ) of the ith category , and ( E ) ; is called the
sample covariance matrix of the ith category . The ( X ) i and ( 2 ) i are reasonable
...
where N . is the number of patterns in the training subset X ; ; ( X ) ; is called the
sample mean ( or center of gravity ) of the ith category , and ( E ) ; is called the
sample covariance matrix of the ith category . The ( X ) i and ( 2 ) i are reasonable
...
Sivu 59
derived from the training set as if they were the known means and covariance
matrices . If we assume appropriate probability distributions for the unknown
mean vectors and covariance matrices , we can derive a training process which
makes ...
derived from the training set as if they were the known means and covariance
matrices . If we assume appropriate probability distributions for the unknown
mean vectors and covariance matrices , we can derive a training process which
makes ...
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Sisältö
Preface vii | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
TRAINING THEOREMS | 79 |
Tekijänoikeudet | |
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adjusted apply assume bank belonging to category called changes Chapter cluster committee components consider consists contains correction corresponding covariance decision surfaces define denote density depends derivation described Development discriminant functions discussed distance distribution element equal error-correction estimates example exists expression FIGURE fixed given implemented important initial layered machine linear dichotomies linear machine linearly separable matrix measurements negative networks normal Note optimum origin parameters partition pattern classifier pattern hyperplane pattern space pattern vector piecewise linear plane points positive presented probability problem proof properties proved PWL machine quadric reduced regions respect response rule sample mean selection separable shown side solution space Stanford step Suppose theorem theory threshold training methods training procedure training sequence training subsets transformation values weight vectors zero
Kirjaluettelon tiedot
| Otsikko | Learning Machines: Foundations of Trainable Pattern-classifying Systems McGraw-Hill series in systems science |
| Kirjoittaja | Nils J. Nilsson |
| Painos | kuvitettu |
| Kustantaja | McGraw-Hill, 1965 |
| Pituus | 137 sivua |
| Vie sitaatti | BiBTeX EndNote RefMan |